Entity IP mapping

ABSTRACT

Systems and methods for mapping IP addresses to an entity include receiving at least one domain name associated with the entity. Embodiments may further include determining one or more variations of the at least one domain name based on analysis of domain name data collected from a plurality of domain name data sources that mention a variation of the at least one domain name. Some embodiments may also include identifying one or more IP addresses pointed to by the one or more variations of the entity&#39;s domain name based on analysis of IP address data collected from a plurality of IP address data sources. Additional embodiments include assigning weights to each of the identified one or more IP addresses and creating a mapping of IP addresses to associate with the entity based on analysis of the weighted one or more IP addresses.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to U.S. Provisional Patent ApplicationNo. 62/091,477 entitled “CORPORATE IP ADDRESS DISCOVERY THROUGHSUBSIDIARY AND INTERNAL SYSTEM MAPPING SYSTEM AND METHOD,” filed on Dec.13, 2014, and also claims priority to U.S. Provisional PatentApplication No. 62/091,478 entitled “CORPORATE CYBER SECURITYBENCHMARKING AS A SERVICE SYSTEM AND METHOD,” filed on Dec. 13, 2014.The entire contents of both are incorporated herein by reference.

FIELD OF THE DISCLOSURE

This disclosure generally relates to corporate cybersecurity technology.More specifically, this disclosure relates to mapping Internet Protocol(IP) addresses to an entity to assist in benchmarking the entity'scybersecurity risk.

BACKGROUND

An entity, such as an organization, corporation, or governmental entity,may have a number of IP addresses allocated to it through whichcomputers may be used for internal and external communication. For someentities, employees at different locations may communicate with eachother using different network service providers, such as InternetService Providers (ISPs). Some entities may also be accessed usingthird-party hosting providers and virtualized cloud services, whichoften assign part of their own allocated IP addresses to the entityinstead of one of the IP addresses already allocated to the entity. As aresult, an entity may not know each of the different IP addressesthrough which employees and computers of the entity communicate.

SUMMARY

A scorecard system may create a mapping of IP addresses that areassociated with an entity to improve accuracy in the calculation of theentity's cybersecurity risk. For example, according to one embodiment, amethod for mapping IP addresses to an entity may include receiving, by aprocessor, at least one domain name associated with the entity. Themethod may also include determining, by the processor, one or morevariations of the at least one domain name based on analysis of domainname data collected from a plurality of domain name data sources thatmention a variation of the at least one domain name. The method mayfurther include identifying, by the processor, one or more IP addressespointed to by the one or more variations of the entity's domain namebased on analysis of IP address data collected from a plurality of IPaddress data sources. The method may also include assigning, by theprocessor, a weight to each of the identified one or more IP addressesbased on a correlation between each of the identified one or more IPaddresses and the one or more variations of the at least one domainname. The method may further include creating, by the processor, amapping of IP addresses to associate with the entity based on analysisof the weighted one or more IP addresses.

According to another embodiment, a computer program product includes anon-transitory computer-readable medium comprising instructions which,when executed by a processor of a computing system, cause the processorto perform the step of receiving at least one domain name associatedwith the entity. The medium may also include instructions which causethe processor to perform the step of determining one or more variationsof the at least one domain name based on analysis of domain name datacollected from a plurality of domain name data sources that mention avariation of the at least one domain name. The medium may furtherinclude instructions which cause the processor to perform the step ofidentifying one or more IP addresses pointed to by the one or morevariations of the entity's domain name based on analysis of IP addressdata collected from a plurality of IP address data sources. The mediummay also include instructions which cause the processor to perform thestep of assigning a weight to each of the identified one or more IPaddresses based on a correlation between each of the identified one ormore IP addresses and the one or more variations of the at least onedomain name. The medium may further include instructions which cause theprocessor to perform the step of creating a mapping of IP addresses toassociate with the entity based on analysis of the weighted one or moreIP addresses.

According to yet another embodiment, an apparatus includes a memory anda processor coupled to the memory. The processor can be configured toexecute the step of receiving at least one domain name associated withthe entity. The processor may also be configured to execute the step ofdetermining one or more variations of the at least one domain name basedon analysis of domain name data collected from a plurality of domainname data sources that mention a variation of the at least one domainname. The processor can be configured to execute the step of identifyingone or more IP addresses pointed to by the one or more variations of theentity's domain name based on analysis of IP address data collected froma plurality of IP address data sources. The processor may also beconfigured to execute the step of assigning a weight to each of theidentified one or more IP addresses based on a correlation between eachof the identified one or more IP addresses and the one or morevariations of the at least one domain name. The processor can beconfigured to execute the step of creating a mapping of IP addresses toassociate with the entity based on analysis of the weighted one or moreIP addresses.

The foregoing has outlined rather broadly the features and technicaladvantages of the present invention in order that the detaileddescription of the invention that follows can be better understood.Additional features and advantages of the invention will be describedhereinafter that form the subject of the claims of the invention. Itshould be appreciated by those skilled in the art that the concepts andspecific embodiments disclosed can be readily utilized as a basis formodifying or designing other structures for carrying out the samepurposes of the present invention. It should also be realized by thoseskilled in the art that such equivalent constructions do not depart fromthe spirit and scope of the invention as set forth in the appendedclaims. The novel features that are believed to be characteristic of theinvention, both as to its organization and method of operation, togetherwith further objects and advantages will be better understood from thefollowing description when considered in connection with theaccompanying figures. It is to be expressly understood, however, thateach of the figures is provided for the purpose of illustration anddescription only and is not intended as a definition of the limits ofthe present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

For a more complete understanding of the disclosure, reference is madeto the following FIGURES taken in conjunction with their accompanyingdescriptions:

FIG. 1 is a block diagram of a network that includes a scorecard server,data sources, and an entity with a cybersecurity risk according to anembodiment;

FIG. 2 is a block diagram of a system for calculating and benchmarkingan entity's cybersecurity risk according to an embodiment;

FIG. 2B is a block diagram of underlying components of a system formapping IP addresses to an entity according to an embodiment; and

FIG. 3 is a flow chart of a method for mapping IP addresses to an entityaccording to an embodiment.

FIG. 4 is a flow diagram illustrating IP mapping with respect to one ormore entities according to an embodiment.

DETAILED DESCRIPTION

The IP addresses associated with an entity, including IP addressesassociated with subsidiary and/or internal systems of the entity, may bedetermined based solely from knowledge of a domain name of an entity.Knowledge of the IP addresses associated with an entity may be usefulfor a variety of applications. For example, as illustrated in FIGS. 1-2,a mapping of the IP addresses associated with an entity may be used todetermine a cybersecurity risk for an entity.

An IP address, as used herein, is a numerical label assigned to adevice, such as a computer or printer, participating in a computernetwork that uses the Internet Protocol for communication. An IP addressprimarily serves two principal functions: (1) identification of a hostor network interface, and (2) location addressing. IP addresses aretypically binary numbers that are usually stored in text files anddisplayed in human-readable notations, such as 172.16.254.1 (for IPv4),and 2001:db8:0:1234:0:567:8:1 (for IPv6).

Certain units described in this specification have been labeled asmodules in order to more particularly emphasize their implementationindependence. A module is “[a] self-contained hardware or softwarecomponent that interacts with a larger system.” Alan Freedman, “TheComputer Glossary” 268 (8th ed. 1998). A module comprises a machine- ormachines-executable instructions. For example, a module may beimplemented as a hardware circuit comprising custom VLSI circuits orgate arrays, off-the-shelf semiconductors such as logic chips,transistors, or other discrete components. A module may also beimplemented in programmable hardware devices such as field programmablegate arrays, programmable array logic, programmable logic devices or thelike.

Modules may also include software-defined units or instructions, thatwhen executed by a processing machine or device, transform data storedon a data storage device from a first state to a second state. Anidentified module of executable code may, for instance, comprise one ormore physical or logical blocks of computer instructions that may beorganized as an object, procedure, or function. Nevertheless, theexecutables of an identified module need not be physically locatedtogether, but may comprise disparate instructions stored in differentlocations that, when joined logically together, comprise the module, andwhen executed by the processor, achieve the stated data transformation.A module of executable code may be a single instruction, or manyinstructions, and may even be distributed over several different codesegments, among different programs, and/or across several memorydevices. Similarly, operational data may be identified and illustratedherein within modules, and may be embodied in any suitable form andorganized within any suitable type of data structure. The operationaldata may be collected as a single data set, or may be distributed overdifferent locations including over different storage devices.

In the following description, numerous specific details are provided,such as examples of programming, software modules, user selections,network transactions, database queries, database structures, hardwaremodules, hardware circuits, hardware chips, etc., to provide a thoroughunderstanding of the present embodiments. One skilled in the relevantart will recognize, however, that the invention may be practiced withoutone or more of the specific details, or with other methods, components,materials, and so forth. In other instances, well-known structures,materials, or operations are not shown or described in detail to avoidobscuring aspects of the invention.

FIG. 1 is a block diagram of network 100 that includes a scorecardserver 110, a communication network 120, an entity server 130, an entity140, data sources 150, and user station 160. The scorecard server 110includes one or more servers that, according to one embodiment, areconfigured to perform several of the functions described herein. One ormore of the servers comprising the scorecard server 110 include memory,storage hardware, software residing thereon, and one or more processorsconfigured to perform functions associated with network 100. Forexample, components comprising user station 160, such as CPU 162, can beused to interface and/or implement scorecard server 110. One of skill inthe art will readily recognize that different server and computerarchitectures can be utilized to implement scorecard server 110 and thatscorecard server 110 is not limited to a particular architecture so longas the hardware implementing scorecard server 110 supports the functionsof the scorecard system disclosed herein.

The communication network 120 facilitates communications of data betweenthe scorecard server 110 and the data sources 150. The communicationnetwork 120 can also facilitate communications of data between thescorecard server 110 and other servers/processors, such as entity server130. The communication network 120 includes any type of communicationsnetwork, such as a direct PC-to-PC connection, a local area network(LAN), a wide area network (WAN), a modem-to-modem connection, theInternet, a combination of the above, or any other communicationsnetwork now known or later developed within the networking arts whichpermits two or more computers to communicate.

The entity server 130 includes the servers which the entity 140 uses tosupport its operations and which the scorecard server 110 accesses tocollect further information to calculate and benchmark an entity'scybersecurity risk. The data sources 150 include the sources from whichthe scorecard server 110 collects information to calculate and benchmarkan entity's cybersecurity risk.

The Entity 140 includes any organization, company, corporation, or groupof individuals. For example, and not limitation, one entity may be acorporation with thousands of employees and headquarters in New YorkCity, while another entity may be a group of one or more individualsassociated with a website and having headquarters in a residential home.

Data Sources 150 includes any source of data accessible over Network120. For example, and not limitation, one source of data can include awebsite associated with a company, while another source of data may bean online database of various information. In general, the data sources150 may be sources of any kind of data, such as domain name data, socialmedia data, multimedia data, IP address data, and the like. One of skillin the art would readily recognize that data sources 150 are not limitedto a particular data source, and that any source from which data mayretrieved may serve as a data source so long as it can be accessed bynetwork 120.

With respect to user station 160, the central processing unit (“CPU”)161 is coupled to the system bus 162. The CPU 161 can be a generalpurpose CPU or microprocessor performing the functions of the scorecardserver 110, a graphics processing unit (“GPU”), and/or microcontroller.Embodiments are not restricted by the architecture of the CPU 161 solong as the CPU 161, whether directly or indirectly, supports theoperations described herein. The CPU 161 is one component may executethe various described logical instructions.

The user station 160 also comprises random access memory (RAM) 163,which can be synchronous RAM (SRAM), dynamic RAM (DRAM), synchronousdynamic RAM (SDRAM), or the like. The user station 160 may utilize RAM163 to store the various data structures used by a software application.The user station 160 also comprises read only memory (ROM) 164 which canbe PROM, EPROM, EEPROM, optical storage, or the like. The ROM may storeconfiguration information for booting the user station 160. The RAM 163and the ROM 164 hold user and system data, and both the RAM 163 and theROM 164 can be randomly accessed.

The user station 160 also comprises an input/output (I/O) adapter 165, acommunications adapter 166, a user interface adapter 167, and a displayadapter 168. The I/O adapter 165 and/or the user interface adapter 167may, in certain embodiments, enable a user to interact with the userstation 160. In a further embodiment, the display adapter 168 maydisplay a graphical user interface (GUI) associated with a software orweb-based application on a display device 169, such as a monitor ortouch screen.

The I/O adapter 165 may couple one or more storage devices 170, such asone or more of a hard drive, a solid state storage device, a flashdrive, a compact disc (CD) drive, a floppy disk drive, and a tape drive,to the user station 160. Also, the data storage 170 can be a separateserver coupled to the user station 160 through a network connection tothe I/O adapter 165. The communications adapter 166 can be adapted tocouple the user station 160 to a network, which can be one or more of aLAN, WAN, and/or the Internet. The user interface adapter 167 couplesuser input devices, such as a keyboard 171, a pointing device 172,and/or a touch screen (not shown) to the user station 160. The displayadapter 168 can be driven by the CPU 161 to control the display on thedisplay device 169. Any of the devices 161-168 can be physical and/orlogical.

The concepts described herein are not limited to the architecture ofuser station 160. Rather, the user station 160 is provided as an exampleof one type of computing device that can be adapted to perform thefunctions of a server and/or the user interface device 165. For example,any suitable processor-based device can be utilized including, withoutlimitation, personal data assistants (PDAs), tablet computers,smartphones, computer game consoles, and multi-processor servers.Moreover, the systems and methods of the present disclosure can beimplemented on application specific integrated circuits (ASIC), verylarge scale integrated (VLSI) circuits, or other circuitry. In fact,persons of ordinary skill in the art may utilize any number of suitablestructures capable of executing logical operations according to thedescribed embodiments.

It should be appreciated that user station 160, or certain componentsthereof, may reside at, or be installed in, different locations withinnetwork 100. According to the illustrated embodiment, user station 160directly interfaces with scorecard server 110. Such an embodiment isconducive for an individual or user not directly associated with entity140 to effectuate computation of a cybersecurity risk and/or benchmarkof same for that entity. However, in other embodiments, one or moreusers located at entity 140 or locations directly associated with same,may effectuate computation of a cybersecurity risk and/or benchmark ofsame for that entity. In such an embodiment, user station 160 (or atleast certain components thereof) may directly interface with entityservers 130. Likewise, entity servers 130 may comprise the hardwareand/or software found in scorecard server 110 in the illustratedembodiment. Importantly, the features necessary to compute cybersecurityrisk scores and benchmarks can be collocated within network 100 ordistributed across, e.g., scorecard server 110 and entity servers 130,and user station(s) 160.

FIG. 2 is a block diagram of a system for calculating and benchmarkingan entity's cybersecurity risk according to an embodiment. Such a systemis described in detail in co-owned, U.S. Patent Application No.14/702,661, filed May 1, 2015 and entitled “CALCULATING AND BENCHMARKINGAN ENTITY'S CYBERSECURITY RISK SCORE,” [the disclosure of which isincorporated herein by reference in its entirety. System 200 can beimplemented with one or more computing devices, such as scorecard server110, entity servers 130, and user station(s) 160 illustrated in FIG. 1.System 200 comprises a security signal collection module 210, acontextualization and attribution module 220, and a benchmarking module230.

Security signal collection module 210 collects one or more types of datathat relate to the cybersecurity risks associated with an entity.Security signal collection module 210 comprises submodules that collectdifferent types of data from a predefined “threat sphere.” The threatsphere may change depending on the entity for which a cybersecurity riskscore is calculated, and may further change according to the goalsand/or objectives of the entity. In any event, the threat sphere istypically defined to include sources of information that likelycomprise, generate, are responsible for, or otherwise correspond to dataindicative of an entity's cybersecurity risk. Accordingly, each moduleor submodule that collects data corresponds to one more channels or datafeeds from sources comprising the threat sphere.

According to the illustrated embodiment, security signal collectionmodule 210 comprises a social engineering collection module 201, amalware and botnet infection collection module 202, an applicationvulnerabilities collection module 203, a breach history collectionmodule 204, a network exploits collection module 205, a DNS Healthcollection module 206, a patching cadence collection module 207, and aleaked credentials collection module 208.

Security signal collection module 210 can also comprises a hacker forummonitoring module 209 for collecting data from hacker forums can alsoand an endpoint security analysis module 211 for collecting endpointdata.

Security signal collection module 210 can also comprises modules forspecifying when data is collected and how data is associated with anentity. For example, the security signal collection module 210 comprisesa continuous Internet scans module 212 for performing continuous scansof Internet data to collect data associated with an entity. The securitysignal collection module 210 can also comprises a real-time scanscollection module 213 for collecting data in real time, such ascollecting real-time threat intelligence/data and collecting data inreal time from a malicious IP feed, which can include digesting 2000+bad (IPS) per second. The security signal collection module 210 can alsocomprises an IP Mapping module 214 to reliably identify IP addressesassociated with an entity. By mapping IP addresses to an entity, datacollected the Internet over one or more channels comprising the threatsphere (or beyond) can be determined to be associated with, orattributable to, the given entity.

Contextualization and attribution module 220 contextualizes datacollected by the security signal collection module 210. Thecontextualization and attribution module 220 comprises an extractionmodule 222 to extract data relevant to cybersecurity of a given entityfrom the collected data. The contextualization and attribution module220 can also comprises a normalization module 224 and a weighting module226 to normalize and/or weight a preliminary security score determinedbased on a raw scoring of the extracted security data. The normalizationand/or weighting of a preliminary score may depend on multiple factors,such as, for example, the size of the entity, the relationship betweenthe extracted information and overall security performance, and the typeof data collected.

The contextualization and attribution module 220 can also comprises amachine learning module 228 to identify and update which factors mostsignificantly affect an entity's cybersecurity. This information can beused to further contextualize the collected data. For example, thesecurity scores identified as being the most relevant may then benormalized and/or weighted to account for their relevancy. Thecontextualization process can also comprises applying temporaladjustments to security data or calculated security scores based on thetime span between an event that generated the security data and thecurrent date. In some embodiments, contextualization can also comprisesvalidating threats, such as, for example, by confirming that an eventcreating data that indicates the presence of a malware event is in facta malware event. Further aspects of the contextualization submodules aredescribed in detail below.

Benchmarking module 230 calculates an overall cybersecurity risk scorefor an entity, as well as a benchmark based on cybersecurity performancemetrics. The computed benchmark may further comprise a percentileranking for the entity. For example, the benchmarking module 230comprises a scoring module 232 to obtain the overall cybersecurity riskscore for an entity based on the contextualization of the entity'ssecurity data and processing of scores for each of the different typesof security data collected for the entity.

The benchmarking module 230 can also comprises a percentiles module 234to determine a percentile ranking for the entity which provides anindication of how the entity's cybersecurity fairs with respect tosimilar companies in the same industry. Further aspects of thebenchmarking submodules are described in detail below. A scorecardserver, such as scorecard server 100 from FIG. 1, may utilize one ormore of the submodules in the security signal collection 210,contextualization 220, and benchmarking 230 modules to score andbenchmark an entity's cybersecurity risk.

As is apparent from FIG. 1 and FIG. 2, a mapping of the IP addressesassociated with an entity may be useful to improve the accuracy withwhich a scorecard system calculates a cybersecurity score for an entity.FIG. 2B is a block diagram of underlying components of a system formapping IP addresses to an entity, and FIG. 3 is a flow chartillustrating a method for mapping IP addresses to an entity using theunderlying components of the system for mapping IP addresses to anentity. It is noted that embodiments of method 300 can be implementedwith the systems described with respect to FIGS. 1-2, in particular theunderlying components illustrated in FIG. 2B. For example, a processordisclosed in method 300 may correspond to a processor within a scorecardserver disclosed in this disclosure. Specifically, method 300 includes,at block 302, receiving at least one domain name associated with theentity. For example, scorecard system 200 can implement reception module219A to receive a URL associated with an entity. The URL may include thedomain name for the entity. The URL, and in particular the domain name,may be input by a user accessing scorecard system 200, for example, auser accessing scorecard system 200 via user station 160 illustrated inFIG. 1.

At block 304, method 300 includes determining one or more variations ofthe at least one domain name. For example, scorecard system 200 canimplement domain name variations module 219B to determine one or morevariations of a received domain name. In some embodiments, determinationof the variations of the domain name may be based on analysis of domainname data collected from a plurality of domain name data sources thatmention a variation of the domain name. As noted with respect to FIG. 1,data sources 150 from which domain name data may be gathered include anysources of data which include information about the domain names used ormentioned in the data source. Examples, and not limitations, of datasources include databases, such as Freebase™ and CrunchBase©, onlineservices, such as LinkedIn©, and general informational websites, such asCNN.com. For example, given a domain “gs.com,” scorecard system 200 maycollect domain name data from multiple data sources across the Internet.The scorecard system 200 may analyze the domain name data collected fromthe data sources to determine one or more variations of the domain name,such as “gs.com” and “goldmansachs.com.” In general, the domain namedata may also include general mentions of an entity in a data source.For example, given a domain “gs.com,” domain name variations may alsoinclude “Goldman Sachs, Inc.” and “The Goldman Sachs,” each of which maypoint to IP addresses. The information collected from all the datasources may be correlated to determine the most accurate variations ofthe domain names associated with an entity. For example, a machinelearning algorithm may be developed to correlate the domain name datacollected from all the data sources to determine the most accuratevariations of the domain names associated with the entity. In someembodiments, the scorecard system 200 may also suffix or appendcharacters to an identified domain name to aid in the determination ofthe domain name variations.

At block 306, method 300 includes identifying one or more IP addressespointed to by the one or more variations of the entity's domain name.For example, scorecard system 200 can implement IP address collectionmodule 219C to identify IP addresses pointed to by domain name data. Insome embodiments, identification of the one or more IP addresses may bebased on analysis of IP address data collected from a plurality of IPaddress data sources. Examples, and not limitations, of data sourcesfrom which IP addresses may be collected include IP registries, such asthe American Registry for Internet Numbers (ARIN), the European IPNetworks (RIPE), the Latin America and Caribbean Network InformationCentre (LACNIC), the African Network Information Center (AFRINIC), andthe Asia-Pacific Network Information Centre (APNIC). Other data sourcesinclude third-party IP address databases. In some embodiments, thescorecard system 200 may also analyze data in Domain Name System (DNS)records, Border Gateway Protocol (BGP) records, Secure Socket Layer(SSL) certificates, security scanners, such as Nmap, content deliverynetwork (CDN) databases, and known security IP addresses, such ashoneypots and sinkholes owned by security companies. From analysis ofdata collected from the data sources, the scorecard system can identifythe IP addresses pointed to by the different variations of the entity'sdomain name.

At block 308, method 300 includes assigning, by the processor, a weightto each of the identified one or more IP addresses. For example,scorecard system 200 can implement IP weighting module 219D to weigh theidentified IP addresses. In some embodiments, weighting may be based ona correlation between each of the identified one or more IP addressesand the one or more variations of the at least one domain name. In oneembodiment, a good correlation may consist of an IP address that ispointed to by multiple variations of the domain name data. In otherwords, weighting may include assigning a larger weight to an IP addressof the identified one or more IP addresses that maps to multiple of theone or more variations of the at least one domain name than the weightassigned to an IP address of the identified one or more IP addressesthat maps to only one of the one or more variations of the at least onedomain name. For example, in one embodiment, a mapping Ci may consist ofa single mapping between an IP address and a domain name data variation.For multiple mappings, such as C1, C2, . . . , Cn, the number of timesan IP address maps to a different variation of the domain name data maybe tracked. Based on the number of times an IP address maps to adifferent variation of the domain name data for an entity, a weightbetween 0 and 1 can be assigned to the IP address. A higher weight maybe assigned to IP addresses pointed to multiple times than IP addressespointed to once.

Weighting with weighting module 219D may also include weighting based onthe reliability of the domain name variation determined for an entity.For example, if the scorecard system 200 had to perform truncation orappending to obtain the domain name variation or if the domain namevariation is mentioned only once in only a single data source, then thescorecard system 200 may assign a lower weight to the IP address pointedto by the domain name variation. In other words, a good indication thatan IP address is associated with an entity is that multiple variationsfor the entity's domain name point to the same IP address. In contrast,if the domain name pointing to an IP address is mentioned in numerousdatabases in association with the company or if the domain namevariation is one specifically provided by an employee of the company,then the scorecard system 200 may assign a larger weight to the IPaddress. In another embodiment, weighing with weighting module 219D mayalso include weighting based on the specific type of entity network withwhich the collected IP addresses are associated. For example, an IPaddress associated with a guest network or WiFi network of the entitymay be assigned a lower weight than an IP address associated with anintranet network of the entity.

The scorecard system 200 may also include a machine learning algorithmto perform the weighting disclosed herein. For example, the scorecardsystem 200 may utilize a machine learning algorithm to, in addition toimplement the other weighting routines disclosed above, analyzeanonymized fingerprints of browsers when analyzing IP addresses obtainedfrom online advertisements or e-mails. One of skill in the art wouldreadily recognize that the machine learning algorithm is not limited toimplementing only the weighting factors disclosed herein, and that manyother factors may be incorporated into the machine learning algorithm solong as the machine learning algorithm utilizes the factors to weightthe IP addresses association with domain name variations of an entity.

At block 310, method 300 includes creating a mapping of IP addresses toassociate with the entity based on analysis of the weighted one or moreIP addresses. For example, scorecard system 200 can implement IP mappingcreation module 219E to create the mapping. In particular, the scorecardsystem 200 may create the IP mapping by selecting only the IP addressesthat are most reliably associated with the entity for inclusion into theIP mapping. For example, in one embodiment, the scorecard system 200 mayset a minimum weight threshold for IP addresses to be included in thecreated IP mapping. IP addresses with weights higher than then minimumthreshold, for example IP addresses pointed to by domain name variationsmentioned in numerous databases in association with the company orspecifically provided by an employee of the company, may be included inthe created IP mapping. IP addresses with weights below the minimumthreshold, for example IP addresses pointed to by unreliable domain namevariations, may be excluded from inclusion in the created IP mapping. Insome embodiments, the created mapping of IP addresses may be adjusted ormaintained up-to-date by periodically executing the reception 219A,domain name variations 219B, IP address collection 219C, weighting 219D,and IP mapping creation 219E modules as disclosed herein.

The IP mapping for an entity, such as the IP mapping created at block310 using IP mapping creation module 219E, may be adjusted for a varietyof reasons. For example, in one embodiment, a representative of anentity may provide specific IP addresses for inclusion in the IP mappingfor the entity or the representative of the entity may specify which IPaddresses in the created IP mapping should not be included in the IPmapping. In other words, the scorecard system 200 adjusts the createdmapping of IP addresses based on user input. In some embodiments, therepresentative may input the IP addresses to include or exclude byaccessing the scorecard system 200 via user station 160. The scorecardsystem may then modify the IP mapping based on the information providedby the entity representative. In some embodiments, the scorecard system200 may also exclude from the IP mapping IP addresses associated withnetworks other than the entity network, such as a Wi-Fi, third-partyhosting, guest, or security network. The scorecard system 200 mayassociate IP addresses with the other networks based on user inputprovided on user station 160. The scorecard system may also associate IPaddresses to the other networks based on the weighting performed byweighting module 219D. For example, the machine learning algorithmutilized during weighting may have identified networks not associatedwith the entity and weighted the IP addresses associated with suchnetworks lower than other IP addresses. As another example, the machinelearning algorithm may have detected IP addresses not having access todata internal to the entity and assigned lower weights to those IPaddresses.

The scorecard system 200 may also determine the IP addresses to beexcluded from the IP mapping during creation of an IP mapping withoutuser input. For example, in one embodiment, IP addresses associated withsecurity providers, such as CloudFlare and Akamai, may be excluded fromthe created IP mapping because they are associated with the securityprovider and not the entity. To identify IP addresses associated withsecurity providers and IP addresses behind the security provider thatare associated with an entity, the scorecard system 200 may utilize IPaddress collection module 219C to collect IP address information fromunusual data sources. For example, the scorecard system 200 may collectIP address information from alternative domain data sources which maynot be configured properly. As another example, the scorecard system 200may collect and analyze data transmission headers associated with thesecurity providers. In yet another example, the scorecard system 200 mayread IP addresses from TCP port 80. In another embodiment, the scorecardsystem 200 may collect the IP address information directly from thesecurity provider.

The scorecard system 200 may also determine IP addresses to be excludedfrom the created IP mapping by determining whether the collected IPaddresses are associated with a WiFi or guest network provided by theentity. In some embodiments, the entity may provide the scorecard system200 with the WiFi or guest networks provided by the entity. In otherembodiments, the scorecard system may determine the networks duringexecution of the IP address collection module 219C by collecting datafor data sources that specify WiFi and guest networks available by theentity. The scorecard system 200 may also perform an Nmap banner grabduring IP address collection to identify whether the IP address isassociated with a guest network of the entity.

The IP mapping created with IP mapping creation module 219E may beassociated with a subnetwork of an entity's network because someentities, such as network hosting entities, may have multiple networks.In one embodiment, the desired IP addresses to be associated with theentity may be only those IP addresses associated with the entity'snetwork. In another embodiment, the desired IP addresses to beassociated with the entity may be the IP addresses that are hosted forother entities. The scorecard system 200 may create the different IPmappings based on a variety of factors. For example, the scorecardsystem 200 may be provided with information by the entity whichspecifies which collected IP addresses should be associated with theentity's network and which IP addresses should be associated with theentity's hosting network. In another embodiment, the scorecard system200 may look at whether a collected IP address is associated with ahosted virtual machine to determine in which IP mapping the IP addressshould be placed. In other embodiments, the scorecard system 200 may usethe machine learning algorithm utilized while executing weighting module219D to identify factors that may indicate that an IP address isassociated with a hosted IP address as opposed to an IP address of thenetwork. The machine learning algorithm may develop the factors andincorporate them into the algorithm to further weigh the IP addresses.

The schematic flow chart diagram of FIG. 3 is generally set forth as alogical flow chart diagram. As such, the depicted order and labeledsteps are indicative of aspects of the disclosed method. Other steps andmethods can be conceived that are equivalent in function, logic, oreffect to one or more steps, or portions thereof, of the illustratedmethod. Additionally, the format and symbols employed are provided toexplain the logical steps of the method and are understood not to limitthe scope of the method. Although various arrow types and line types canbe employed in the flow chart diagram, they are understood not to limitthe scope of the corresponding method. Indeed, some arrows or otherconnectors can be used to indicate only the logical flow of the method.For instance, an arrow may indicate a waiting or monitoring period ofunspecified duration between enumerated steps of the depicted method.Additionally, the order in which a particular method occurs may or maynot strictly adhere to the order of the corresponding steps shown.

FIG. 4 is a flow diagram illustrating IP mapping implemented byscorecard system 200 according to an embodiment of the disclosure. It isnoted that embodiments of flow 400 can be implemented with the systemsdescribed with respect to FIGS. 1-2, in particular the underlyingcomponents illustrated in FIG. 2B. For example, a processor disclosed inflow 400 may correspond to a processor within a scorecard serverdisclosed in this disclosure. Referring to flow 400, at block 402,scorecard system 200 receives a new domain associated with an entity.For example, scorecard system 200 can implement reception module 219A toreceive a URL associated with an entity.

At blocks 404 a-404 c, flow 400 includes queries 404 a-404 c of datafeeds to determine the different variations of the received domain nameand to identify the IP addresses pointed to by the domain name data inthe data feeds. Data feeds may correspond to one or more data sources,such as one or more of data sources 150 illustrated in FIG. 1. In someembodiments, to implement queries 404 a-404 c, scorecard system 200 canimplement domain name variations module 219B and/or IP addresscollection module 219C. In some embodiments, scorecard system 200 mayimplement at least one of queries 404 a-404 c by collecting data fromone or more data sources and then implementing together the functions ofdomain name variations module 219B and IP address collection module 219Cdescribed above. In another embodiment, scorecard system 200 mayimplement at least one of queries 404 a-404 c by implementing domainvariations module 219B before implementing IP collection module 219C. Inyet another embodiment, scorecard system 200 may implement at least oneof queries 404 a-404 c by implementing IP collection module 219C beforeimplementing domain name variations module 219B.

After performing queries 404 a-404 c, scorecard system 200 may performfurther analysis of the identified IP addresses to create an IP mappingfor the entity. For example, in one embodiment, scorecard system 200 mayimplement weighting module 219D to weigh the identified IP addresses andIP mapping creation module 219E to create the IP mapping based at leaston the weighting of the IP addresses. Based on the further analysisperformed by implementing the functions of weighting module 219D and IPmapping creation module 219E, scorecard system 200 may create the IPmapping for an entity. For example, in one embodiment, the results ofthe further analysis may identify which IP addresses can be reliablyincluded in the IP mapping for the entity and which IP addresses requireadditional analysis to determine whether the IP addresses should beincluded in the IP mapping for the entity or excluded from the IPmapping for the entity.

As noted with respect to FIG. 3, the scorecard system 200 may identifywhich IP addresses are most reliably associated with the entity based ona weight threshold. For example, in one embodiment, the scorecard system200 may set a minimum weight threshold for IP addresses to be includedin the created IP mapping. IP addresses with weights higher than theminimum threshold, for example IP addresses pointed to by domain namevariations mentioned in numerous databases in association with thecompany or specifically provided by an employee of the company, may beindicated as reliably associated with the entity. In addition, IPaddresses found in the majority of data feeds/sources may also beidentified as reliably associated with the entity. In contrast, IPaddresses with weights below the minimum threshold, for example IPaddresses pointed to by unreliable domain name variations, may beindicated as not reliably associated with the entity. In addition, IPaddresses not found in the majority of data feeds/sources may beindicated as not reliably associated with the entity.

As indicated in flow 400, IP addresses indicated as reliably associatedwith the entity may be grouped together, for example in bucket one 406.IP addresses in bucket one 406 may be automatically included in theentity's initial IP mapping 410. In contrast, the IP addresses indicatedas not initially being associated with the entity may be groupedtogether in a separate group, for example in bucket two 408, to undergofurther processing, as indicated in further processing block 412 in flow400.

In some embodiments, further processing 412 performed on IP addressesgrouped into bucket two 408 may include implementation of variousaspects of IP address collection module 219C, weighting module 219D, andIP mapping creation module 219E, as described in blocks 306, 308, and310 of method 300, respectively. For example, further processing 412 mayinclude implementing IP collection module 219C to collect IP specificinformation from IP specific data sources, such as ARIN, RIPE, LACNIC,AFRINIC, and APNIC. In addition, further processing may includeanalyzing data in DNS records, such as performing reverse DNSautomation, and analyzing data in BGP records, SSL certificates,security scanners, such as Nmap, CDN databases, and known security IPaddresses, such as honeypots and sinkholes owned by security companies.

Scorecard system 200 may identify further IP addresses to include in theinitial IP mapping 410 based on the further processing 412 performed onthe IP addresses in bucket two 408. For example, scorecard system 200may identify IP addresses matching perfectly to an IP address associatedwith the entity as indicated by the IP addresses collected for theentity during the performance of further processing 412. As an example,scorecard system 200 may group together the IP addresses matchingperfectly into bucket three 414 and include the IP addresses in theinitial IP mapping 410.

In other embodiments, those IP addresses that were not identified duringfurther processing 412 as matching perfectly to an IP address known tobe associated with the entity based on the IP addresses collected usingIP address collection module 219C may undergo additional processing,such as at processing block 416. The processing performed on IPaddresses at block 416 may include implementation of various aspects ofIP address collection module 219C, weighting module 219D, and IP mappingcreation module 219E, as described in blocks 306, 308, and 310 of method300, respectively. For example, the processing at block 416 may includeimplementation of machine learning, such as the machine learningprocessing disclosed in the discussion regarding weighting module 219Dand block 308.

In some embodiments, the IP addresses placed in the initial mapping 410may be verified as being associated with the entity through analysis ofinformation provided for the entity on data sources, such as Freebasedatabases 418 and social media websites, for example LinkedIn 420. Insome embodiments, the initial IP mapping 410, either before or after theoptional aforementioned verification processing, may be identified asthe final IP mapping created for an entity, such as the final IP mappingcreated for an entity at block 310 of FIG. 3.

If implemented in firmware and/or software, the functions describedabove can be stored as one or more instructions or code on acomputer-readable medium. Examples include non-transitorycomputer-readable media encoded with a data structure andcomputer-readable media encoded with a computer program.Computer-readable media includes physical computer storage media. Astorage medium can be any available medium that can be accessed by acomputer. By way of example, and not limitation, such computer-readablemedia can comprise RAM, ROM, EEPROM, CD-ROM or other optical diskstorage, magnetic disk storage or other magnetic storage devices, or anyother medium that can be used to store desired program code in the formof instructions or data structures and that can be accessed by acomputer. Disk and disc includes compact discs (CD), laser discs,optical discs, digital versatile discs (DVD), floppy disks and blu-raydiscs. Generally, disks reproduce data magnetically, and discs reproducedata optically. Combinations of the above should also be included withinthe scope of computer-readable media.

In addition to storage on computer-readable medium, instructions and/ordata can be provided as signals on transmission media included in acommunication apparatus. For example, a communication apparatus includesa transceiver having signals indicative of instructions and data. Theinstructions and data can be configured to cause one or more processorsto implement the functions outlined in the claims.

Although the present disclosure and its advantages have been describedin detail, it should be understood that various changes, substitutionsand alterations can be made herein without departing from the spirit andscope of the disclosure as defined by the appended claims. Moreover, thescope of the present application is not intended to be limited to theparticular embodiments of the process, machine, manufacture, compositionof matter, means, methods and steps described in the specification. Asone of ordinary skill in the art will readily appreciate from thepresent invention, disclosure, machines, manufacture, compositions ofmatter, means, methods, or steps, presently existing or later to bedeveloped that perform substantially the same function or achievesubstantially the same result as the corresponding embodiments describedherein can be utilized according to the present disclosure. Accordingly,the appended claims are intended to include within their scope suchprocesses, machines, manufacture, compositions of matter, means,methods, or steps.

What is claimed is:
 1. A method for mapping Internet Protocol (IP)addresses to an entity, the method comprising: determining, by aprocessor operating at a node in a network, one or more variations of atleast one domain name based on analysis of domain name data collectedfrom a plurality of domain name data sources over the network thatmention a variation of the at least one domain name; identifying, by theprocessor, one or more IP addresses pointed to by the one or morevariations of the entity's at least one domain name based on analysis ofIP address data collected from a plurality of IP address data sources;and assigning, by the processor, a weight to each of the identified oneor more IP addresses based on a correlation between each of theidentified one or more IP addresses and the one or more variations ofthe at least one domain name; wherein assigning a weight comprises:assigning a first weight to an IP address of the identified one or moreIP addresses that maps to multiple of the one or more variations of theat least one domain name and assigning a second weight to an IP addressof the identified one or more IP addresses that maps to only one of theone or more variations of the at least one domain name; where the firstweight is greater than the second weight.
 2. The method of claim 1,wherein identifying one or more IP addresses comprises collecting one ormore IP addresses that map to the one or more variations of the at leastone domain name.
 3. The method of claim 1, further comprising: mappingIP addresses to the entity based on analysis of the first weighted oneor more IP addresses and the second weighted or more IP addresses. 4.The method of claim 3, further comprising adjusting the created mappingof IP addresses based on user input.
 5. The method of claim 3, furthercomprising adjusting or maintaining up-to-date the created mapping of IPaddresses by periodically performing the foregoing steps of receiving,determining, identifying, assigning, and creating.
 6. The method ofclaim 1, further comprising: associating IP addresses with one or morenetworks that do not include the entity based on a detection of IPaddresses not having access to data internal to the entity.
 7. Themethod of claim 6, wherein the networks other than the entity compriseat least one of a Wi-Fi, third-party hosting, guest, or securitynetwork.
 8. A computer program product, comprising: a non-transitorycomputer-readable medium comprising instructions which, when executed bya processor of a computing system, cause the processor to perform thesteps of: determining one or more variations of at least one domain namebased on analysis of domain name data collected from a plurality ofdomain name data sources that mention a variation of the at least onedomain name; identifying one or more IP addresses pointed to by the oneor more variations of the at least one domain name based on analysis ofIP address data collected from a plurality of IP address data sources;and assigning a weight to each of the identified one or more IPaddresses based on a correlation between each of the identified one ormore IP addresses and the one or more variations of the at least onedomain name, wherein assigning a weight comprises: assigning a firstweight to an IP address of the identified one or more IP addresses thatmaps to multiple of the one or more variations of the at least onedomain name, and assigning a second weight to an IP address of theidentified one or more IP addresses that maps to only one of the one ormore variations of the at least one domain name; where the first weightis greater than the second weight.
 9. The computer program product ofclaim 8, wherein identifying one or more IP addresses comprisescollecting one or more IP addresses that map to the one or morevariations of the at least one domain name.
 10. The computer programproduct of claim 8, wherein the medium further comprises instructions tocause the processor further perform: mapping IP addresses to the entitybased on analysis of the first weighted one or more IP addresses and thesecond weighted one or more IP addresses.
 11. The computer programproduct of claim 8, wherein the medium further comprises instructions tocause the processor to perform: adjusting the created mapping of IPaddresses based on user input.
 12. The computer program product of claim8, wherein the medium further comprises instructions to cause theprocessor to perform: adjusting or maintaining up-to-date the createdmapping of IP addresses by periodically performing the foregoing stepsof receiving, determining, identifying, assigning, and creating.
 13. Thecomputer program product of claim 10, wherein the medium furthercomprises instructions to cause the processor to perform: associating IPaddresses with one or more networks that do not include the entity basedon a detection of IP addresses not having access to data internal to theentity.
 14. The computer program product of claim 8, wherein thenetworks other than the entity comprise at least one of a Wi-Fi,third-party hosting, guest, or security network.
 15. An apparatus,comprising: a memory; and a processor coupled to the memory, theprocessor configured to execute the steps of: determining one or morevariations of the at least one domain name based on analysis of domainname data collected from a plurality of domain name data sources thatmention a variation of the at least one domain name; identifying one ormore IP addresses pointed to by the one or more variations of theentity's domain name based on analysis of IP address data collected froma plurality of IP address data sources; and assigning a weight to eachof the identified one or more IP addresses based on a correlationbetween each of the identified one or more IP addresses and the one ormore variations of the at least one domain name, wherein assigning aweight comprises: assigning a first weight to an IP address of theidentified one or more IP addresses that maps to multiple of the one ormore variations of the at least one domain name, and assigning a secondweight to an IP address of the identified one or more IP addresses thatmaps to only one of the one or more variations of the at least onedomain name; where the first weight is greater than the second weight.16. The apparatus of claim 15, wherein identifying one or more IPaddresses comprises collecting one or more IP addresses that map to theone or more variations of the at least one domain name.
 17. Theapparatus of claim 15, wherein the processor is further configured to:map IP addresses to the entity based on analysis of the first weightedone or more IP addresses and the second weighted one or more IPaddresses.
 18. The apparatus of claim 15, wherein the processor isfurther configured to: adjust the created mapping of IP addresses basedon user input.
 19. The apparatus of claim 15, wherein the processor isfurther configured to: adjust or maintain the created mapping of IPaddresses by periodically performing the foregoing steps of receiving,determining, identifying, assigning, and creating.
 20. The apparatus ofclaim 15, wherein the processor is further configured to: associate IPaddresses with one or more networks that do not include the entity basedon a detection of IP addresses not having access to data internal to theentity, wherein the one or more networks comprise at least one of aWi-Fi, third-party hosting, guest, or security network.